Cardiac arrhythmia identification and characterization is necessary for management of cardiac disorders and irregularities. Usually, signal analysis based on electrophysiological activity (such as on ECG signals and intra-cardiac electrograms) and time domain parameters (such as magnitude and voltage) of the waveforms are utilized for cardiac arrhythmia detection and pathology diagnosis. This involves examining P wave disorders for atrial fibrillation (AF) and ST segment changes for myocardial ischemia and infarction, for example. However, known systems for cardiac arrhythmia identification and analysis based on ECG signals are subjective and need extensive expertise and clinical experience for accurate interpretation and appropriate cardiac rhythm management.
Coronary Artery Disease (CAD) and heart-related problems and cardiac arrhythmias are common and serious. Early arrhythmia recognition and characterization, such as associated with myocardial ischemia and infarction, is valuable for rhythm management of cardiac disorders and irregularities. Currently waveform morphologies and time domain parameter analysis of depolarization and repolarization, such as P wave, QRS complex, ST segment, T wave, are used for cardiac arrhythmia monitoring and identification. However, known systems based on waveform and time domain parameter analyses, are often subjective and time-consuming, and require expertise and clinical experience for accurate interpretation and proper cardiac rhythm management. Some known systems apply more sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis, symbolic complexity analysis and nonlinear entropy evaluation and focus on generating a new pathology index for qualitative cardiac arrhythmia characterization. These known systems fail to provide comprehensive cardiac electrophysiological function and activity interpretation, tissue mapping and arrhythmia localization.
Additionally, known systems typically analyze waveform characteristics (such as amplitude, latency,) or frequency domain characteristics (such as power, spectrum) which may not efficiently identify small signal changes in a partial portion (P wave, QRS complex, ST segment) of a cardiac cycle. These small changes are usually invisible in a signal wave morphology display and need extensive clinical expertise for identification. Consequently, known systems may fail to identify arrhythmia and have a high rate of false alarm indication. Known systems based on amplitude (voltage) changes and variation analysis may be inadequate to diagnose pathology and fail to accurately explain clinical information and associate signal frequency variation, for example. Known systems may fail to predict a pathological trend, especially in early stage of tissue malfunction and may not efficiently analyze and identify a real time growing trend of cardiac arrhythmias, such as a pathology trend from low risk to medium, and then to a high risk (severe and fatal) rhythm (especially for an arrhythmia such as a VT growing trend). Further, known cardiac function monitoring systems may lack sensitivity and stability for arrhythmia analysis for patient status evaluation and may generate inaccurate and unreliable data because of noise and artifacts. Environmental noise and patient movement artifacts including electrical interference, can distort a waveform and make it difficult to detect R wave and ST segment elevation accurately, and result in a false alarm. A system according to invention principles addresses these deficiencies and related problems.